Journal article Open Access

Online Non-Collocated Estimation of Payload and Articular Stress for Real-Time Human Ergonomy Assessment

Claudia Latella; Kourosh Darvish; Yeshasvi Tirupachuri; Prashanth Ramadoss; Lorenzo Rapetti; Silvio Traversaro; Daniele Pucci

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  <identifier identifierType="URL">https://www.openaccessrepository.it/record/142791</identifier>
  <creators>
    <creator>
      <creatorName>Claudia Latella</creatorName>
    </creator>
    <creator>
      <creatorName>Kourosh Darvish</creatorName>
    </creator>
    <creator>
      <creatorName>Yeshasvi Tirupachuri</creatorName>
    </creator>
    <creator>
      <creatorName>Prashanth Ramadoss</creatorName>
    </creator>
    <creator>
      <creatorName>Lorenzo Rapetti</creatorName>
    </creator>
    <creator>
      <creatorName>Silvio Traversaro</creatorName>
    </creator>
    <creator>
      <creatorName>Daniele Pucci</creatorName>
    </creator>
  </creators>
  <titles>
    <title>Online Non-Collocated Estimation of Payload and Articular Stress for Real-Time Human Ergonomy Assessment</title>
  </titles>
  <publisher>INFN Open Access Repository</publisher>
  <publicationYear>2021</publicationYear>
  <subjects>
    <subject>EC</subject>
    <subject>Research and Innovation action</subject>
    <subject>European Commission</subject>
    <subject>H2020</subject>
    <subject>Rural Digital Europe</subject>
    <subject>General Engineering</subject>
    <subject>General Materials Science</subject>
    <subject>General Computer Science</subject>
  </subjects>
  <dates>
    <date dateType="Issued">2021-01-01</date>
  </dates>
  <resourceType resourceTypeGeneral="Text">Journal article</resourceType>
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    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsIdenticalTo">10.1109/access.2021.3109238</relatedIdentifier>
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  <rightsList>
    <rights rightsURI="https://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
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  <descriptions>
    <description descriptionType="Abstract">Improving the quality of work for human beings is receiving a lot of attention from multiple research communities. In particular, digital transformation in human factors and ergonomics is going to empower the next generation of the socio-technical workforce. The use of wearable sensors, collaborative robots, and exoskeletons, coupled with novel technologies for the real-time assessment of human ergonomy forms the crux of this digital transformation. In this direction, this paper focuses on the open problem of estimating the interaction wrench experienced at the human extremities (such as hands), where the feasibility of direct sensor measurements is not practical. We refer to our approach as non-collocated wrench estimation, as we aim to estimate the wrench at known contact locations but without using any direct force-torque sensor measurements at these known locations. We achieve this by extending the formulation of stochastic inverse dynamics for humans by considering a centroidal dynamics constraint to perform a reliable non-collocated estimation of interaction wrench and the joint torques (articular stress) experienced as a direct consequence of the interaction. Our approach of non-collocated estimation is thoroughly validated in terms of payload estimation and articular stress estimation through validation and experimental scenarios involving dynamic human motions like walking.</description>
  </descriptions>
  <fundingReferences>
    <fundingReference>
      <funderName>European Commission</funderName>
      <funderIdentifier funderIdentifierType="Crossref Funder ID">10.13039/501100000780</funderIdentifier>
      <awardNumber awardURI="info:eu-repo/grantAgreement/EC/H2020/731540/">731540</awardNumber>
      <awardTitle>Advancing Anticipatory Behaviors in Dyadic Human-Robot Collaboration</awardTitle>
    </fundingReference>
  </fundingReferences>
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Publication date:
January 1, 2021
DOI:
10.1109/access.2021.3109238

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Keyword(s):
EC Research and Innovation action European Commission H2020 Rural Digital Europe General Engineering General Materials Science General Computer Science
Grants:
European Commission:
  • An.Dy - Advancing Anticipatory Behaviors in Dyadic Human-Robot Collaboration (731540)
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License (for files):
Creative Commons Attribution 4.0

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